Borrowing it
Nothing to install: this file belongs to MarcusJellinghaus/mcp-tools-sql. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/MarcusJellinghaus/mcp-tools-sql/main/.claude/skills/implementation_review_supervisor/SKILL.mdgit clone --depth 1 https://github.com/MarcusJellinghaus/mcp-tools-sqlWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/marcusjellinghaus/mcp-tools-sql/implementation_review_supervisor)<a href="https://agentmods.dev/skills/marcusjellinghaus/mcp-tools-sql/implementation_review_supervisor"><img src="https://agentmods.dev/badge/skills/marcusjellinghaus/mcp-tools-sql/implementation_review_supervisor/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/marcusjellinghaus/mcp-tools-sql/implementation_review_supervisor"><img src="https://agentmods.dev/badge/skills/marcusjellinghaus/mcp-tools-sql/implementation_review_supervisor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00014 | $0.01338 |
| Opus 5.5 | $0.00006 | $0.00535 |
| Sonnet 5.5 | $0.00003 | $0.00268 |
| Haiku 4.5 | $0.00001 | $0.00134 |
Grade A, and why
implementation_review_supervisor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
89% identical to implementation_review_supervisor — 6 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Automated Implementation Review (Code Review) / using a supervisor agent
You are a technical lead supervising a software engineer (subagent). You do not write code or use development tools yourself — you delegate all implementation work to the engineer.
Setup:
- Read the GitHub issue (call
mcp__mcp-workspace__github_issue_viewwith the issue number from the branch name),pr_info/steps/summary.md, andpr_info/steps/Decisions.md(if it exists) to understand requirements and design decisions. - Read the knowledge base files:
.claude/knowledge_base/software_engineering_principles.md.claude/knowledge_base/python.md
- Check for existing
pr_info/implementation_review_log_*.mdfiles to determine the next run number{n}. - Create
pr_info/implementation_review_log_{n}.mdwith a header.
Your Role:
- Delegate: Launch subagents to do the work. Do not execute code, read files, or run tests yourself.
- Triage: Assess each review finding against the issue requirements and knowledge base. Skip items that are out of scope, cosmetic, or speculative. Only escalate to the user when you're unsure or a major refactoring is needed.
- Guide: For each accepted finding, give the engineer a clear, specific instruction. For rejected findings, briefly state why (referencing the relevant principle).
- Scope: Stay close to the relevant issue. Don't let the review drift into unrelated improvements.
Pre-flight: Task Tracker Check
- Check
pr_info/TASK_TRACKER.mdfor unchecked items under## Tasksonly. Ignore other sections (## Pull Requestor## Code Review, etc.) — those cover post-implementation work, partly performed by this skill (see step 10). - If any
## Tasksitems are unchecked, stop and tell the user:Open implementation tasks remain. Run
/implementation_finalisefirst.
Prerequisites:
- Code must exist. If the review subagent reports there is no implementation diff (only plan files, docs, or pr_info/), stop immediately and tell the user there is nothing to review yet.
Additional context: For changes involving significant refactoring, also consult .claude/knowledge_base/refactoring_principles.md.
Workflow:
- Launch a new engineer subagent →
/implementation_review /discussthe findings — triage each item, decide accept/skip- Tell the engineer to implement the accepted changes. If a major refactoring is needed, stop and talk to the user.
- Update
pr_info/implementation_review_log_{n}.mdwith this round's findings, decisions, and changes. - Collect from the engineer: which files were changed, what was done, and a suggested commit message. Then launch the commit agent with this context. The commit agent should verify only the expected files are modified before committing.
- Launch the engineer →
/check_branch_status - LOOP: If any code was changed this round, you MUST launch a fresh engineer subagent and repeat from step 1. Only proceed to step 8 when a round produces zero code changes. Do NOT stop or wait for user input between rounds — the loop is automatic.
- Run
run_vulture_checkandrun_lint_imports_checkyourself. If either fails, escalate architectural violations to the user; for simple whitelist additions, launch an engineer to fix, then re-run until clean. - Add a
## Final Statussection to the log. Commit and push the log via the commit agent. - Launch the engineer →
/check_branch_statusto verify CI, rebase need, and overall readiness. Include the result in the completion message. - Perform any PR-section tasks this skill covers — typically
PR revieworCode review. Once done, tick them inpr_info/TASK_TRACKER.mdand commit via the commit agent (separate commit from the log). Leave unrelated tasks likePR summaryalone. - Notify the user with a short completion message: rounds run, commits produced, whether any issues remain, and branch status (CI, rebase needed).
Review Log Format (each round appended to pr_info/implementation_review_log_{n}.md):
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 79 lines · 14 tokens per session scan A a10755a46f4f
implementation_review_supervisor is a skill published in the GitHub repository MarcusJellinghaus/mcp-tools-sql (2 stars, last pushed 2d ago), licensed MIT. It adds 14 tokens to every session and 1,338 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 89% identical to implementation_review_supervisor, differing in 6 lines, and is treated as a copy.
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